Abstract
Participation in conferences is an important part of every scientific career. Conferences provide an opportunity for a fast dissemination of latest results, discussion and exchange of ideas, and broadening of scientists’ collaboration network. The decision to participate in a conference depends on several factors like the location, cost, popularity of keynote speakers, and the scientist’s association with the community. Here we discuss and formulate the problem of discovering how a scientist’s previous participation affects her/his future participations in the same conference series. We develop a stochastic model to examine scientists’ participation patterns in conferences and compare our model with data from six conferences across various scientific fields and communities. Our model shows that the probability for a scientist to participate in a given conference series strongly depends on the balance between the number of participations and non-participations during his/her early connections with the community. An active participation in a conference series strengthens the scientist’s association with that particular conference community and thus increases the probability of future participations.
Highlights
Social data at a large scale is nowadays available over the internet
We collected and filtered information about abstracts presented at the American Physical Society March Meeting (APSMM), American Physical Society April Meeting (APSAM), Society for Industrial and Applied Mathematics Annual Meetings (SIAM), Neural Information Processing Systems Conference (NIPS), International Conference on Supercomputing (ICS) and Annual International Conference on Research in Computational Molecular Biology (RECOMB)
When it comes to the meeting size it can vary from a few dozens, like ICS and RECOMB, to several thousands of participants at APSMM
Summary
Social data at a large scale is nowadays available over the internet. Researchers are making the best use of these data to find trends, statistics and patterns, which sometime reveal as robust features, similar to ‘laws’ in natural science. A huge community of researchers [1] including mathematicians, statisticians, computer scientists, theoretical physicists, sociologists, economists, financial analysts, geographers, anthropologists, and biologists of various sub-disciplines have contributed to a larger, developing field, commonly known as ‘computational social science’ [2]. Statistical mechanics, which has been proved to be versatile in modeling phenomena across different areas of physics, and beyond, seems to be the most desired tool even for the above emerging discipline [3, 4].
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